Gini Agent is a personal AI-agent runtime that manages conversations, runs, tasks, approvals, memory, skills, jobs, tools, traces, audit events, and runtime health across its clients. It is for an agent that can remember, learn from task outcomes, and operate through desktop or mobile interfaces with user controls. Catalogue add-ons extend the runtime with skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Open-Curiosity/gini-agent --skill people-crmgit clone --depth 1 https://github.com/Open-Curiosity/gini-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/open-curiosity/gini-agent/people-crm)<a href="https://agentmods.dev/skills/open-curiosity/gini-agent/people-crm"><img src="https://agentmods.dev/badge/skills/open-curiosity/gini-agent/people-crm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/open-curiosity/gini-agent/people-crm"><img src="https://agentmods.dev/badge/skills/open-curiosity/gini-agent/people-crm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.01344 |
| Opus 5 | $0.00013 | $0.00672 |
| Sonnet 5 | $0.00005 | $0.00269 |
| Haiku 4.5 | $0.00003 | $0.00134 |
Grade A, and why
people-crm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
People CRM
Use your own database (db_* tools) to keep the user's professional network as structured, exhaustively-queryable records — import their connections once, then answer "who do I know at X", track who people are over time, and map who-knows-whom. This is the right tool because contact questions demand complete answers; long-term memory recall is a fuzzy sample and will miss people.
When to Use
- The user gives you their LinkedIn connections (a
Connections.csvexport) or any contact/roster list and wants to "remember", "load", or "keep track of" their network. - They ask to find / list / count people by company, role, location, or how they're connected ("who do I know at Stripe", "how many founders do I know", "who could introduce me to someone at Google").
- They describe a person to track ("add my friend Tom, he founded Acme", "Sara moved to Stripe as Head of Eng").
When NOT to Use
- One-off facts with no roster ("remember my wife's birthday") → that's ordinary memory, not a CRM.
- A few unrelated notes about a single person where the user won't query across people.
Getting the data in
LinkedIn → Settings → Data Privacy → Get a copy of your data → Connections emails a Connections.csv. When the user attaches it (or any CSV/XLSX), import it — do NOT read the rows into the chat and retype them:
db_import path="uploads/<id>/Connections.csv" table="contacts"
db_import skips the export's preamble lines automatically and creates columns from the header, so contacts ends up with: first_name, last_name, url, email_address, company, position, connected_on (all TEXT). Re-importing is safe with recreate: true to start clean. Confirm with db_schema.
Tip: LinkedIn dates look like
05 Jun 2024. If the user wants date-range queries, add an ISO column once:db_execute "ALTER TABLE contacts ADD COLUMN connected_iso TEXT"then populate it with anUPDATEusingsubstr/CASEoverconnected_on.
Querying the network (always exhaustive)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 110 lines · 25 tokens per session scan A 044306332227
people-crm is a skill published in the GitHub repository Open-Curiosity/gini-agent (2,142 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,344 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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